Abstract
Représentations spatiales du jeu de données et dbmss.
Ce code crée des cartes en 2D et 3D du Parc Omnisport Suzanne Lenglen.
load("data/POSL.RData")
library("dbmss")
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
rename(PointType = GenrEsp) %>%
as.wmppp(unitname = c("meter", "meters")) ->
POSL_VA_wmppp
POSL_VA_wmppp %>%
autoplot(alpha = 0.8, xlab = "X Lambert", ylab = "Y Lambert") +
labs(title = "Arbres vivants et abattus du POSL") +
labs(color = "Espèce", size = "Surface terrière")
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
rename(PointType = EspeceFrancais) %>%
as.wmppp(unitname = c("meter", "meters")) ->
POSL_Esp_wmppp
POSL_Esp_wmppp %>%
autoplot(alpha = 0.8, xlab = "X Lambert", ylab = "Y Lambert") +
labs(title = "Arbres du POSL") + labs(color = "Espèce",
size = "Surface terrière")
BDD_Vivants_et_Abattus_renseignes_POSL %>%
ggplot(aes(x = X, y = Y)) + geom_point() + geom_density_2d() +
ggtitle("Arbres vivants et abattus du POSL")
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
rename(PointType = Etat) %>%
as.wmppp(unitname = c("meter", "meters")) ->
BDD_V2022_A2022_Poids_POSL_wmppp
### Carte
BDD_V2022_A2022_Poids_POSL_wmppp %>%
autoplot(alpha = 0.8, xlab = "X Lambert", ylab = "Y Lambert") +
labs(title = "Arbres vivants et abattus du POSL") +
labs(color = "Espèce", size = "Surface terrière")
# Création du jeu de points
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Arbres abattus seulement
filter(Etat == "Abattu") %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
rename(PointType = MotifAbattage) %>%
as.wmppp(unitname = c("meter", "meters")) ->
POSL_A_wmppp
### Carte
POSL_A_wmppp %>%
autoplot(alpha = 0.8, xlab = "X Lambert", ylab = "Y Lambert") +
labs(title = "Arbres abattus du POSL") + labs(color = "Cause",
size = "Surface terrière")
Density_POSL <- density(POSL_VA_wmppp, bw.diggle(POSL_VA_wmppp),
dimyx = c(128, 128))
par(mar = c(0, 0, 0, 0))
persp.im(Density_POSL, shade = 0.5, scale = FALSE,
axes = TRUE, border = NA, box = FALSE, phi = 60,
main = "") -> Projection
K_Abattus <- KEnvelope(POSL_A_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(K_Abattus, legend = FALSE, xlab = "Distance (mètres)",
ylab = "K(Distance)", main = "K de Ripley \n Arbres abattus en 2021 dans le POSL")
legend("topleft", c("K", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.04)
autoplot(K_Abattus)
Conclusion : interactions détectée entre les arbres abattus : attraction. Les arbres abattus sont plus concentrés qu’une distribution complètement aléatoire.
persp(density(POSL_A_wmppp),
col="aliceblue", # couleur triste = arbres abattus
theta = 40, phi = 20,
xlab = "Lambert X", ylab = "Lambert Y", zlab = "Densité",
main = "Densité arbres abattus en 2021 dans le POSL")
K_Vivants <- KEnvelope(POSL_Esp_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(K_Vivants, legend = FALSE, xlab = "Distance (mètres)",
ylab = "K(Distance)", main = "K de Ripley \n Arbres vivants en 2022 dans le POSL")
legend("topleft", c("K", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.04)
autoplot(K_Vivants)
Conclusion : interactions détectée entre les arbres vivants : attraction. Les arbres vivants sont plus concentrés qu’une distribution complètement aléatoire.
m_Abattus <- mEnvelope(BDD_V2022_A2022_Poids_POSL_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Abattu",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_Abattus, xlab = "Distance (mètres)", ylab = "m(Distance)",
legend = FALSE, main = "m de Lang et al. \n Arbres abattus par rapport aux vivants dans le POSL")
legend("topright", c("m", expression(alpha == "1%, ICg "),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = -0.02)
autoplot(m_Abattus)
Conclusions :
M_Abattus <- MEnvelope(BDD_V2022_A2022_Poids_POSL_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Abattu",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_Abattus, legend = FALSE, xlab = "Distance (mètres)",
ylab = "M(Distance)", xlim = c(0, 50), main = "M de Marcon et Puech \n Arbres abattus par rapport aux vivants dans le POSL")
legend("topright", c("M", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.1)
plot(M_Abattus)
Conclusion : un peu conc sign autour de 5m.
Attention : attention warning message lors de la compil.
Warning messages: 1: In FUN(newX[, i], ...) :
aucun argument trouvé pour min ; Inf est renvoyé"
Modification du code : utiliser POSL_A_wmppp plutôt que BDD_V2022_A2022_Poids_POSL_wmppp2.
M_Declin <- MEnvelope(POSL_A_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, ReferenceType = "Arbre en déclin physiologique irréversible",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_Declin, legend = FALSE, xlim = c(0, 30), xlab = "Distance (mètres)",
ylab = "M(Distance)", main = "M de Marcon et Puech \n Arbres en déclin par rapport aux vivants et malades dans le POSL")
legend("topright", c("M", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.1)
autoplot(M_Declin)
Conclusion : NS ??? Attention : warnings sur FUN
m_Declin <- mEnvelope(POSL_A_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, ReferenceType = "Arbre en déclin physiologique irréversible",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_Declin, legend = FALSE, xlab = "Distance (mètres)",
ylab = "m(Distance)", main = "m de Lang et al. \n Arbres en déclin par rapport aux vivants et malades dans le POSL")
legend("topright", c("m", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.05)
autoplot(m_Declin)
Conclusion : conc sign jusqu’à 100m puis jusqu’à 250m.
M_Malades <- MEnvelope(POSL_A_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, ReferenceType = "Foyer d'agent(s) pathogène(s)",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_Malades, legend = FALSE, xlim = c(0, 75), xlab = "Distance (mètres)",
ylab = "M(Distance)", main = "M de Marcon et Puech \n Arbres malades par rapport aux vivants \n et autres abattus dans le POSL")
legend("topright", c("M", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.1)
autoplot(M_Malades)
Conclusion : conc à petites distances (environ 10m) ??? Attention : warnings FUN
m_Malades <- mEnvelope(POSL_A_wmppp, NumberOfSimulations = 1000,
Alpha = 0.01, ReferenceType = "Foyer d'agent(s) pathogène(s)",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_Malades, legend = FALSE, xlab = "Distance (mètres)",
ylab = "m(Distance)", main = "m de Lang et al. \n Arbres malades par rapport aux vivants\n et aux autres abattus dans le POSL")
legend("topright", c("m", expression(alpha == "1%, ICg"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.05)
autoplot(m_Malades)
Création de la BDD sans les 25 arbres en déclin donc contient vivant et abattus-patho :
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
filter(MotifAbattage != "Arbre en déclin physiologique irréversible") %>%
mutate(PointType = ifelse(Etat == "Vivant", "Vivant",
"Foyer d'agent(s) pathogène(s)")) %>%
as.wmppp(unitname = c("meter", "meters")) ->
BDD_V2022_A2022_Poids_POSL_sans_abattus_declin_wmppp
autoplot(BDD_V2022_A2022_Poids_POSL_sans_abattus_declin_wmppp)
Création de la BDD sans les 23 arbres foyer patho donc reste uniquement vivants et en déclin:
BDD_Vivants_et_Abattus_renseignes_POSL %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
filter(MotifAbattage != "Foyer d'agent(s) pathogène(s)") %>%
mutate(PointType = ifelse(Etat == "Vivant", "Vivant",
"Arbre en déclin physiologique irréversible")) %>%
as.wmppp(unitname = c("meter", "meters")) ->
BDD_V2022_A2022_Poids_POSL_sans_patho_wmppp
autoplot(BDD_V2022_A2022_Poids_POSL_sans_patho_wmppp)
M_Malades <- MEnvelope(BDD_V2022_A2022_Poids_POSL_sans_abattus_declin_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Foyer d'agent(s) pathogène(s)",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_Malades, legend = FALSE, xlim = c(0, 20), xlab = "Distance (mètres)",
ylab = "M(Distance)", main = "M de Marcon et Puech \n Arbres malades par rapport aux vivants (uniquement) dans le POSL")
legend("topright", c("M", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.1)
autoplot(M_Malades)
Conclusion : conc sign entre 6m et 8m et M est alors = 10 ou 11 !!! Remarque : warning fun.
m_Malades <- mEnvelope(BDD_V2022_A2022_Poids_POSL_sans_abattus_declin_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Foyer d'agent(s) pathogène(s)",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_Malades, legend = FALSE, xlab = "Distance (mètres)",
ylab = "m(Distance)", main = "m de Lang et al. \n Arbres malades par rapport aux vivants (uniquement) dans le POSL")
legend("topright", c("m", expression(alpha == "1%, ICg"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.05)
autoplot(m_Malades)
M_Declin <- MEnvelope(BDD_V2022_A2022_Poids_POSL_sans_patho_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Arbre en déclin physiologique irréversible",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_Declin, legend = FALSE, xlim = c(0, 30), xlab = "Distance (mètres)",
ylab = "M(Distance)", main = "M de Marcon et Puech \n Arbres en déclin par rapport aux vivants (uniquement) dans le POSL")
legend("topright", c("M", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.1)
autoplot(M_Declin)
m_Declin <- mEnvelope(BDD_V2022_A2022_Poids_POSL_sans_patho_wmppp,
NumberOfSimulations = 1000, Alpha = 0.01, ReferenceType = "Arbre en déclin physiologique irréversible",
SimulationType = "RandomLocation", Global = TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_Declin, legend = FALSE, xlab = "Distance (mètres)",
ylab = "m(Distance)", main = "m de Lang et al. \n Arbres en déclin par rapport aux vivants (uniquement) dans le POSL")
legend("topright", c("m", expression(alpha == "1%, IC global"),
"Centre IC"), col = c("black", "grey", "red"),
lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset = 0.05)
autoplot(m_Declin)
BDD_Vivants_et_Abattus_renseignes_POSL %>%
filter(EspeceFrancais == "Erable") %>%
# Le poids est la surface terrière
mutate(PointWeight = Circonference^2/4/pi) %>%
filter(MotifAbattage != "Arbre en déclin physiologique irréversible") %>%
mutate(PointType = ifelse(Etat == "Vivant", "Vivant",
"Abattu")) %>%
as.wmppp(unitname = c("meter", "meters")) ->
BDD_V2022_A2022_Poids_POSL_erables_VPatho_wmppp
autoplot(BDD_V2022_A2022_Poids_POSL_erables_VPatho_wmppp)
#### M érables foyer patho
M_erables <- MEnvelope(BDD_V2022_A2022_Poids_POSL_erables_VPatho_wmppp,
NumberOfSimulations=1000,
Alpha=0.1, # attention : 10% ici
ReferenceType = "Abattu",
SimulationType = "RandomLocation",
Global=TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(M_erables,
legend = FALSE,
xlim=c(0,30),
xlab="Distance (mètres)", ylab="M(Distance)",
main="M de Marcon et Puech \n Erables malades par rapport aux érables vivants dans le POSL")
legend("topright",c("M", expression(alpha=='10%, IC global'), "Centre IC"),
col = c("black", "grey", "red"), lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset=0.1)
autoplot(M_erables)
Conclusion : sign conc à 6m à 1000 sim et alpha = 10%, très légèrement sign pour alpha = 1%.
m_erables <- mEnvelope(BDD_V2022_A2022_Poids_POSL_erables_VPatho_wmppp,
NumberOfSimulations=1000,
Alpha=0.1, # attention : 10% ici
ReferenceType = "Abattu",
SimulationType = "RandomLocation",
Global=TRUE)
## Generating 1000 simulations by evaluating
## expression ...
## 1, 2, 3, ......10.........20.........30.........40..
## .......50.........60.........70.........80....
## .....90.........100.........110.........120......
## ...130.........140.........150.........160........
## .170.........180.........190.........200.........210
## .........220.........230.........240.........250..
## .......260.........270.........280.........290....
## .....300.........310.........320.........330......
## ...340.........350.........360.........370........
## .380.........390.........400.........410.........420
## .........430.........440.........450.........460..
## .......470.........480.........490.........500....
## .....510.........520.........530.........540......
## ...550.........560.........570.........580........
## .590.........600.........610.........620.........630
## .........640.........650.........660.........670..
## .......680.........690.........700.........710....
## .....720.........730.........740.........750......
## ...760.........770.........780.........790........
## .800.........810.........820.........830.........840
## .........850.........860.........870.........880..
## .......890.........900.........910.........920....
## .....930.........940.........950.........960......
## ...970.........980.........990......... 1000.
##
## Done.
plot(m_erables,
legend = FALSE,
xlab="Distance (mètres)", ylab="m(Distance)",
main="m de Lang et al. \n Erables malades par rapport aux érables vivants dans le POSL")
legend("topright",c("m", expression(alpha=='10%, IC global'), "Centre IC"),
col = c("black", "grey", "red"), lty = c(1, 1, 8), lwd = c(1, 10, 1), bty = "n",
text.col = "black", horiz = FALSE, inset=0.01)
autoplot(m_erables)
Conclusion : NS si alpha 1%, NS le plus souvent si alpha = 10%